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Data Science for Marketing Analytics : Achieve Your Marketing Goals with the Data Analytics Power of Python by Tommy Blanchard, Pranshu Bhatnagar and Debasish Behera (2019, Trade Paperback)

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About this product

Product Identifiers

PublisherPackt Publishing, The Limited

ISBN-101789959411

ISBN-139781789959413

eBay Product ID (ePID)14038572959

Product Key Features

Number of Pages420 Pages

LanguageEnglish

Publication NameData Science for Marketing Analytics : Achieve Your Marketing Goals with the Data Analytics Power of Python

SubjectGeneral, Data Processing, Programming Languages / Python, Marketing / Research

Publication Year2019

TypeTextbook

Subject AreaComputers, Business & Economics

AuthorTommy Blanchard, Pranshu Bhatnagar, Debasish Behera

FormatTrade Paperback

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Item Length3.6 in

Item Width3 in

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Intended AudienceTrade

Table Of ContentTable of Contents Data Preparation and Cleaning Data Exploration and Visualization Unsupervised Learning: Customer Segmentation Choosing the Best Segmentation Approach Predicting Customer Revenue Using Linear Regression Other Regression Techniques and Tools for Evaluation Supervised Learning: Predicting Customer Churn Fine-Tuning Classification Algorithms Modeling Customer Choice

SynopsisExplore new and more sophisticated tools that reduce your marketing analytics efforts and give you precise results Key Features Study new techniques for marketing analytics Explore uses of machine learning to power your marketing analyses Work through each stage of data analytics with the help of multiple examples and exercises Book Description Data Science for Marketing Analytics covers every stage of data analytics, from working with a raw dataset to segmenting a population and modeling different parts of the population based on the segments. The book starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation. As you make your way through the chapters, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding chapters, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. By the end of this book, you will be able to build your own marketing reporting and interactive dashboard solutions. What you will learn Analyze and visualize data in Python using pandas and Matplotlib Study clustering techniques, such as hierarchical and k-means clustering Create customer segments based on manipulated data Predict customer lifetime value using linear regression Use classification algorithms to understand customer choice Optimize classification algorithms to extract maximal information Who this book is for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary., Data Science for Marketing Analytics opens doors to looking at data with a different approach and new tools. Drawing on machine learning and data science concepts, this book broadens the range of tools that you can use to transform the market analysis process.